{"id":190,"date":"2018-01-11T10:29:00","date_gmt":"2018-01-11T10:29:00","guid":{"rendered":"https:\/\/kindsonthegenius.com\/blog\/2018\/01\/11\/what-is-the-difference-between-supervised-and-unsupervised-learning\/"},"modified":"2020-11-05T13:13:12","modified_gmt":"2020-11-05T12:13:12","slug":"what-is-the-difference-between-supervised-and-unsupervised-learning","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/blog\/what-is-the-difference-between-supervised-and-unsupervised-learning\/","title":{"rendered":"What is the Difference Between Supervised and Unsupervised Learning?"},"content":{"rendered":"<div style=\"color: #555555; font-size: 18px; line-height: 30px; text-align: justify;\">\n<div style=\"font-family: 'segoe ui';\">Supervised learning and unsupervised learning are the two branches of Machine Learning.<br \/>In my previous article <a href=\"https:\/\/www.kindsonthegenius.com\/machine-learning-introduction-to-machine-learning-course\/\" target=\"_blank\" rel=\"noopener noreferrer\">Introduction to Machine Learning<\/a>, I discussed the various categories and sub-categories of machine learning.<\/p>\n<div style=\"text-align: center;\"><a href=\"https:\/\/4.bp.blogspot.com\/-Ljt8DsZzFyw\/Wlc6rVwOUrI\/AAAAAAAAAuY\/ndhUJKB94doP4wL2tSODys4_zAfgr-gVwCLcBGAs\/s1600\/Difference-Between-Supervised-Learning-and-Unsupervised%2BLearning.JPG\" style=\"margin-left: 1em; margin-right: 1em;\"><img decoding=\"async\" loading=\"lazy\" border=\"0\" data-original-height=\"384\" data-original-width=\"641\" height=\"191\" src=\"https:\/\/4.bp.blogspot.com\/-Ljt8DsZzFyw\/Wlc6rVwOUrI\/AAAAAAAAAuY\/ndhUJKB94doP4wL2tSODys4_zAfgr-gVwCLcBGAs\/s320\/Difference-Between-Supervised-Learning-and-Unsupervised%2BLearning.JPG\" width=\"320\" \/><\/a><\/div>\n<p>We would begin the discussion with a of Supervise Learning<\/p>\n<p><ins data-ad-client=\"ca-pub-7041870931346451\" data-ad-format=\"fluid\" data-ad-layout=\"in-article\" data-ad-slot=\"8227894917\" style=\"display: block; text-align: center;\"><\/ins><span style=\"color: #45818e;\"><span style=\"font-size: large;\"><b>Supervised Learning<\/b><\/span><\/span><br \/>This is a branch of machine learning that tries to find a relationship in a given training data set.<br \/>In supervised learning, the input consists of a set of input vector together with a corresponding target vectors.<br \/>In supervised learning, you have input variables (x) and out variable (y) and you have to use some algorithm to determine mapping function from the input to the output<\/p>\n<div style=\"text-align: center;\"><i><span style=\"font-family: Georgia, &quot;Times New Roman&quot;, serif;\">&nbsp;y = f(x)<\/span><\/i><\/div>\n<p>When this is done, then given a new input data, you can predict the output.<\/p>\n<p><span style=\"color: #990000;\">Why it is called &#8216;Supervised&#8217;<\/span><br \/>It is called supervised learning because the training data set is considered supervisory, that is it supervises the algorithm or controls the learning process. So if the algorithm, makes a wrong prediction, the training dataset corrects it. So the process is learning from the training\/supervisory data set.<\/p>\n<p><ins data-ad-client=\"ca-pub-7041870931346451\" data-ad-format=\"fluid\" data-ad-layout=\"in-article\" data-ad-slot=\"8227894917\" style=\"display: block; text-align: center;\"><\/ins>Supervised learning is divided into two categories: classification and regression.<\/p>\n<table align=\"center\" cellpadding=\"0\" cellspacing=\"0\" style=\"margin-left: auto; margin-right: auto; text-align: center;\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\"><a href=\"https:\/\/3.bp.blogspot.com\/-iw0ESCW3Qkk\/Wlcp8QGG0pI\/AAAAAAAAAtw\/gNgvTwjb2WgMra5VYeuUmCNLKf0VL7R1wCLcBGAs\/s1600\/digit%2Brecognition.JPG\" style=\"margin-left: auto; margin-right: auto;\"><img decoding=\"async\" loading=\"lazy\" border=\"0\" data-original-height=\"510\" data-original-width=\"549\" height=\"296\" src=\"https:\/\/3.bp.blogspot.com\/-iw0ESCW3Qkk\/Wlcp8QGG0pI\/AAAAAAAAAtw\/gNgvTwjb2WgMra5VYeuUmCNLKf0VL7R1wCLcBGAs\/s320\/digit%2Brecognition.JPG\" width=\"320\" \/><\/a><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Figure 1: Digit recognition example of Classification<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><i>&nbsp;Classification<\/i>: In classification, the objective is to assign each input vector to one of a given number of discrete categories. A typical example of classification is the image recognition problem. The input is a handwritten image made up of a grid of pixels, say 64 x 64. And the aim is to assign each image to one of 10 classes, 1 to 9.<\/p>\n<table align=\"center\" cellpadding=\"0\" cellspacing=\"0\" style=\"margin-left: auto; margin-right: auto; text-align: center;\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\"><a href=\"https:\/\/3.bp.blogspot.com\/-NvCz-8DJ-rw\/WlczHm_RWdI\/AAAAAAAAAuA\/SdQTXsZfvHwGaSmqBp2Tu0MXaLptmYEQwCLcBGAs\/s1600\/Regression.JPG\" style=\"margin-left: auto; margin-right: auto;\"><img decoding=\"async\" loading=\"lazy\" border=\"0\" data-original-height=\"655\" data-original-width=\"986\" height=\"265\" src=\"https:\/\/3.bp.blogspot.com\/-NvCz-8DJ-rw\/WlczHm_RWdI\/AAAAAAAAAuA\/SdQTXsZfvHwGaSmqBp2Tu0MXaLptmYEQwCLcBGAs\/s400\/Regression.JPG\" width=\"400\" \/><\/a><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Figure 2: Linear Regression<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><i>Regression<\/i>: In the case of regression the objective is to find the a relationship among the input variables. Regression analysis helps in understanding how the dependent variable changes with respect to the independent variables.<\/p>\n<p><span style=\"color: #45818e;\"><span style=\"font-size: large;\"><b>Unsupervised Learning<\/b><\/span><\/span><br \/>In unsupervised learning is a type of machine learning algorithm that draws inference from input datasets without corresponding labeled response. So here, you just have a set data values, say, only x, and you need to search for trends withing this dataset.<br \/><ins data-ad-client=\"ca-pub-7041870931346451\" data-ad-format=\"fluid\" data-ad-layout=\"in-article\" data-ad-slot=\"8227894917\" style=\"display: block; text-align: center;\"><\/ins>Unsupervised learning can further be divided into Clustering, Density Extimation and Dimensionality Reduction<\/p>\n<table align=\"center\" cellpadding=\"0\" cellspacing=\"0\" style=\"margin-left: auto; margin-right: auto; text-align: center;\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\"><a href=\"https:\/\/4.bp.blogspot.com\/-0OP0yctuRSg\/Wlc3i_szBQI\/AAAAAAAAAuM\/8usA_d9fBPkTverJiSjZNwkKw_1-osjhwCLcBGAs\/s1600\/Clustering.JPG\" style=\"margin-left: auto; margin-right: auto;\"><img decoding=\"async\" loading=\"lazy\" border=\"0\" data-original-height=\"522\" data-original-width=\"639\" height=\"326\" src=\"https:\/\/4.bp.blogspot.com\/-0OP0yctuRSg\/Wlc3i_szBQI\/AAAAAAAAAuM\/8usA_d9fBPkTverJiSjZNwkKw_1-osjhwCLcBGAs\/s400\/Clustering.JPG\" width=\"400\" \/><\/a><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Figure 3: Clusters within the dataset<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<ul>\n<li><span style=\"color: #990000;\">In <i>clustering<\/i><\/span>, the goal is to find clusters or groups of similar examples withing the data.<\/li>\n<li><span style=\"color: #990000;\"><i>Density estimation<\/i><\/span> has to do with identifying groups of data within the original dataset that belong to the same density distribution.<\/li>\n<li><span style=\"color: #990000;\"><i>Dimensionality Reduction<\/i><\/span> reduces data from a higher dimensional space to lower dimension like 2 or 3 dimensions using the principal components.<\/li>\n<\/ul>\n<p>Summary of the Difference between Supervised Learning and Unsupervised Learning is given in the table below<span style=\"color: #0000ee;\">:<\/span><\/p>\n<p><span style=\"color: #0000ee;\"> <\/span><\/p>\n<table border=\"1\" cellpadding=\"0\" cellspacing=\"0\" style=\"border-collapse: collapse; mso-yfti-tbllook: 1056; width: 552px;\">\n<colgroup>\n<col style=\"mso-width-source: userset; width: 204pt;\" width=\"272\"><\/col>\n<col style=\"mso-width-source: userset; width: 210pt;\" width=\"280\"><\/col>\n<\/colgroup>\n<tbody>\n<tr height=\"39\" style=\"height: 29.2pt; mso-height-source: userset;\">\n<td height=\"39\" style=\"height: 29.2pt; width: 204pt;\" width=\"272\">\n<div style=\"direction: ltr; language: en-GB; margin-bottom: 0pt; margin-left: 0in; margin-top: 0pt; mso-line-break-override: none; punctuation-wrap: hanging; text-align: left; unicode-bidi: embed; word-break: normal;\"><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 16.0pt; font-weight: bold;\">Supervised<\/span><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 16.0pt; font-weight: bold; vertical-align: baseline;\">  Learning<\/span>   <\/div>\n<\/td>\n<td style=\"width: 210pt;\" width=\"280\">\n<div style=\"direction: ltr; language: en-GB; margin-bottom: 0pt; margin-left: 0in; margin-top: 0pt; mso-line-break-override: none; punctuation-wrap: hanging; text-align: left; unicode-bidi: embed; word-break: normal;\"><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 16.0pt; font-weight: bold;\">Unsupervised   Learning<\/span><\/div>\n<\/td>\n<\/tr>\n<tr height=\"39\" style=\"height: 29.2pt; mso-height-source: userset;\">\n<td height=\"39\" style=\"height: 29.2pt; width: 204pt;\" width=\"272\">\n<div style=\"direction: ltr; language: en-GB; margin-bottom: 0pt; margin-left: 0in; margin-top: 0pt; mso-line-break-override: none; punctuation-wrap: hanging; text-align: left; unicode-bidi: embed; word-break: normal;\"><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt;\">Input<\/span><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt; vertical-align: baseline;\">  data is labelled<\/span><\/div>\n<\/td>\n<td style=\"width: 210pt;\" width=\"280\">\n<div style=\"direction: ltr; language: en-GB; margin-bottom: 0pt; margin-left: 0in; margin-top: 0pt; mso-line-break-override: none; punctuation-wrap: hanging; text-align: left; unicode-bidi: embed; word-break: normal;\"><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt;\">Input<\/span><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt; vertical-align: baseline;\">  data is <\/span><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt; vertical-align: baseline;\">unlabeled<\/span><\/div>\n<\/td>\n<\/tr>\n<tr height=\"39\" style=\"height: 29.2pt; mso-height-source: userset;\">\n<td height=\"39\" style=\"height: 29.2pt; width: 204pt;\" width=\"272\">\n<div style=\"direction: ltr; language: en-GB; margin-bottom: 0pt; margin-left: 0in; margin-top: 0pt; mso-line-break-override: none; punctuation-wrap: hanging; text-align: left; unicode-bidi: embed; word-break: normal;\"><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt;\">Uses   training dataset<\/span><\/div>\n<\/td>\n<td style=\"width: 210pt;\" width=\"280\">\n<div style=\"direction: ltr; language: en-GB; margin-bottom: 0pt; margin-left: 0in; margin-top: 0pt; mso-line-break-override: none; punctuation-wrap: hanging; text-align: left; unicode-bidi: embed; word-break: normal;\"><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt;\">Uses<\/span><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt; vertical-align: baseline;\">  just input dataset<\/span><\/div>\n<\/td>\n<\/tr>\n<tr height=\"39\" style=\"height: 29.2pt; mso-height-source: userset;\">\n<td height=\"39\" style=\"height: 29.2pt; width: 204pt;\" width=\"272\">\n<div style=\"direction: ltr; language: en-GB; margin-bottom: 0pt; margin-left: 0in; margin-top: 0pt; mso-line-break-override: none; punctuation-wrap: hanging; text-align: left; unicode-bidi: embed; word-break: normal;\"><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt;\">Used<\/span><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt; vertical-align: baseline;\">  for prediction<\/span><\/div>\n<\/td>\n<td style=\"width: 210pt;\" width=\"280\">\n<div style=\"direction: ltr; language: en-GB; margin-bottom: 0pt; margin-left: 0in; margin-top: 0pt; mso-line-break-override: none; punctuation-wrap: hanging; text-align: left; unicode-bidi: embed; word-break: normal;\"><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt;\">Used   for analysis<\/span><\/div>\n<\/td>\n<\/tr>\n<tr height=\"39\" style=\"height: 29.2pt; mso-height-source: userset;\">\n<td height=\"39\" style=\"height: 29.2pt; width: 204pt;\" width=\"272\">\n<div style=\"direction: ltr; language: en-GB; margin-bottom: 0pt; margin-left: 0in; margin-top: 0pt; mso-line-break-override: none; punctuation-wrap: hanging; text-align: left; unicode-bidi: embed; word-break: normal;\"><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt;\">Classification<\/span><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt; vertical-align: baseline;\">  and regression<\/span><\/div>\n<\/td>\n<td style=\"width: 210pt;\" width=\"280\">\n<div style=\"direction: ltr; language: en-GB; margin-bottom: 0pt; margin-left: 0in; margin-top: 0pt; mso-line-break-override: none; punctuation-wrap: hanging; text-align: left; unicode-bidi: embed; word-break: normal;\"><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt;\">Clustering,   density<\/span><span style=\"color: black; font-family: &quot;trebuchet ms&quot;; font-size: 12.0pt; vertical-align: baseline;\">  estimation and dimensionality reduction<\/span><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Other Machine Learning Resources <\/p><\/div>\n<\/div>\n<p><ins data-ad-client=\"ca-pub-7041870931346451\" data-ad-format=\"fluid\" data-ad-layout=\"in-article\" data-ad-slot=\"8227894917\" style=\"display: block; text-align: center;\"><\/ins><br \/><ins data-ad-client=\"ca-pub-7041870931346451\" data-ad-format=\"fluid\" data-ad-layout=\"in-article\" data-ad-slot=\"8227894917\" style=\"display: block; text-align: center;\"><\/ins><br \/><ins data-ad-client=\"ca-pub-7041870931346451\" data-ad-format=\"fluid\" data-ad-layout=\"in-article\" data-ad-slot=\"8227894917\" style=\"display: block; text-align: center;\"><\/ins><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Supervised learning and unsupervised learning are the two branches of Machine Learning.In my previous article Introduction to Machine Learning, I discussed the various categories and &hellip; <\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"categories":[392],"tags":[],"_links":{"self":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/190"}],"collection":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/comments?post=190"}],"version-history":[{"count":1,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/190\/revisions"}],"predecessor-version":[{"id":1694,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/190\/revisions\/1694"}],"wp:attachment":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/media?parent=190"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/categories?post=190"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/tags?post=190"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}